EP0661826A2 - Codage perceptuel en sous-bandes dans laquelle le rapport signal/masquage est calculés à partir des signaux dans les sous-bandes - Google Patents

Codage perceptuel en sous-bandes dans laquelle le rapport signal/masquage est calculés à partir des signaux dans les sous-bandes Download PDF

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Publication number
EP0661826A2
EP0661826A2 EP94308601A EP94308601A EP0661826A2 EP 0661826 A2 EP0661826 A2 EP 0661826A2 EP 94308601 A EP94308601 A EP 94308601A EP 94308601 A EP94308601 A EP 94308601A EP 0661826 A2 EP0661826 A2 EP 0661826A2
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subband
signal
subbands
processing system
data processing
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Subramania Sudharsanan
Selvarathinam Suthakaran
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International Business Machines Corp
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International Business Machines Corp
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B1/00—Details of transmission systems, not covered by a single one of groups H04B3/00 - H04B13/00; Details of transmission systems not characterised by the medium used for transmission
    • H04B1/66—Details of transmission systems, not covered by a single one of groups H04B3/00 - H04B13/00; Details of transmission systems not characterised by the medium used for transmission for reducing bandwidth of signals; for improving efficiency of transmission
    • H04B1/665—Details of transmission systems, not covered by a single one of groups H04B3/00 - H04B13/00; Details of transmission systems not characterised by the medium used for transmission for reducing bandwidth of signals; for improving efficiency of transmission using psychoacoustic properties of the ear, e.g. masking effect

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  • the present invention relates in general to an improved method and apparatus for efficiently transmitting data from a source apparatus to a receiving apparatus.
  • the present invention relates to a method and apparatus for compressing data for transmission.
  • the present invention relates to a method and apparatus for compressing digital audio data.
  • a system data bus may be utilized to transmit data to and from a central processing unit, direct access storage devices, communications input/output processors (IOPs), and other peripheral devices.
  • IOPs communications input/output processors
  • the bandwidth of a data bus is the rate, expressed in bytes per second, at which data can be conveyed from a source to a target, such as a workstation or other receiving device connected on the bus. Such bandwidth is limited by the electrical characteristics of the transceivers connected to the system data bus, and the electrical characteristics of the system data bus itself.
  • a communication link may be utilized to transmit data from a source processor to a workstation within a distributed data processing system.
  • a communication link also has a finite bandwidth which limits the capacity or volume of information that may be transmitted via the communications link.
  • data transmission capacity is a resource that may be divided among several devices connected to such communication channels. As more devices are connected to such communications channels, and as the volume of data communicated between devices on such channels increases, the need to conserve channel capacity and optimize channel usage becomes increasingly important.
  • Multimedia data is a collection of "time-related" or “time-based” data files which may be utilized to represent video, sound, and animation.
  • Such multimedia data files are typically quite large. For example, at 300 pixels per inch and 24 bits per pixel, an 8 1/2-by-11-inch colour picture requires more than 25 megabytes of data storage.
  • One method of increasing the capacity of the system data bus or the communications link is to transmit data more efficiently by transmitting data in a compressed format.
  • Data compression is the process of eliminating gaps, empty fields, redundancies, and unnecessary data in order to shorten the length of a data file.
  • MPEG Moving Pictures Experts Group
  • ISO International Standards Organization
  • IEC International Electrotechnical Commission
  • the MPEG standards for audio may be found in ISO-IEC/JTC1 SC29/WG11, Coding of Moving Pictures And Associated Audio For Digital Storage Media At Up to About 1.5 Mbits/s - Part 3: Audio , DIS, 11172, April 1992.
  • MPEG sets forth standards for data compression and may be applied to various signals such as audio and video.
  • any data object such as a page of text, an image, a segment of speech or music, or a video sequence
  • the compression of any data object may be thought of as a series of steps, including: (1) a decomposition of that object into a collection of "tokens"; (2) the representation of those tokens by binary strings which have a minimal length in some sense; and (3) the concatenation of the strings in a well defined order.
  • subband coding is employed to compress audio data.
  • the tokens for audio data are subbands.
  • a "subband" is a frequency band in a frequency domain.
  • the present invention provides, in a first aspect, a method in a data processing system for efficiently compressing a digital audio signal, wherein said digital audio signal includes a plurality of samples, said method comprising: separating each of said plurality of samples into a plurality of subbands; predicting a signal to mask ratio for each of said plurality of subbands utilizing a model of relationships between energy values within each of said plurality of subbands and signal to mask ratios values based on a predetermined psychoacoustic model; allocating a number of bits in response to said predicted signal to mask ratio and a preselected bit-rate; and quantizing each of said plurality of subbands based on said number of bits allocated, wherein said digital audio signal may be efficiently compressed.
  • the present invention provides a data processing system for compressing a digital audio signal, wherein said digital audio signal includes a plurality of samples, said data processing system comprising: separation means for separating each of said plurality of samples into a plurality of subbands; prediction means for predicting a signal to mask ratio for each of said plurality of subbands utilizing a model of relationships between energy values within each of said plurality of subbands and signal to mask ratios values based on a predetermined psychoacoustic model; allocation means for allocating a number of bits in response to said predicted signal to mask ratio and a preselected bit-rate; and quantization means for quantizing each of said plurality of subbands based on said number of bits allocated, wherein said digital audio signal may be efficiently compressed.
  • a device is used in determining bit allocation, which in turn provides the required input to enable adaptive quantization of a digital audio signal that has been divided into subbands.
  • the method and system of a preferred embodiment of the present invention permit the efficient compressing of a digital audio signal, wherein the digital audio signal includes a plurality of samples. Each of the samples are separated into a subbands. A signal to mask ratio (SMR) for each the subbands is predicted utilizing a model of relationships between energy values within each of the subbands and SMR values based on a predetermined psychoacoustic model. A number of bits are allocated in response to the predicted SMR. Then each of the subbands are quantized based on the number of bits allocated, wherein the digital audio signal may be efficiently compressed.
  • SMR signal to mask ratio
  • the method proposed by MPEG for compression of digital audio is based on subband coding (SBC).
  • SBC subband coding
  • a SBC scheme initially splits the incoming signal into multiple signals that correspond to various bandwidths that comprise the entire spectrum of the signal. Then the signals are quantized according to either a pre-specified or a dynamic bit-allocation scheme.
  • the compression algorithms that attempt to preserve the original quality as much as possible usually employ a dynamic bit allocation scheme.
  • the bit-allocation is based upon a perceptual model of the human ear.
  • the perceptual model commonly known as a psychoacoustic model, utilizes the spectral information content of the incoming signal and outputs a vector of values that correspond to the signal to mask ratios (SMR) in each subband.
  • SMR signal to mask ratios
  • MPEG recommends two different such models, Psychoacoustic Model 1 (PM1) and Psychoacoustic Model 2 (PM2). More information on MPEG and PM1 and PM2 may be found in ISO-IEC/JTC1 SC29/WG11, Coding Of Moving Pictures And Associated Audio for Digital Storage Media At Up to About 1.5 Mbits/s - Part 3: Audio, DIS, 11172, April 1992.
  • PCM pulse code modulation
  • SMR signal to mask ratio
  • Bit allocation is performed to allocate bits available for storage or transmission of PCM samples in a subband. The number of bits allocated depends on the SMR value computed in block 202 .
  • SMR values are used in conjunction with signal to Noise Ratios (SNR) resulting from quantization of the signal to allocate the number of bits needed for quantization in each subband.
  • SNR signal to Noise Ratios
  • a high SMR results in more bits being allocated, while a low SMR causes less bits to be allocated for encoding.
  • United States Patent No. 4,899,384 teaches table controlled bit allocation in a variable rate subband speech coder
  • United States Patent No. 5,185,800 discloses a bit allocation device for transformed digital audio signals with adaptive quantization based on psychoauditive criterion..
  • PCM samples also are processed utilizing subband analysis, as illustrated in block 208 .
  • Subband analysis involves producing subbands for encoding.
  • the subbands may be selected by the user or specified by an encoding standard, such as MPEG.
  • the subbands may be produced from the PCM samples by filtering the PCM samples with cosine modulated filters to produce the desired subbands. Each filter is employed to separate a subband from the PCM samples.
  • a number of different filters may be utilized to select the desired subbands from the PCM samples, depending on the subbands desired or specified. Examples of various filter designs may be found in H. S.
  • a scale factor is then determined and coded for each of the subbands separated from each of the PCM samples factor, as illustrated in block 210 .
  • the absolute maximum of the 12 samples is taken as the scale factor. To prevent an infinite number of choices for the scale factor, only 64 values are used in Layer I and II. Hence the scale factor value that is higher and closest to this absolute maximum value is chosen and indicated to the decoder by an index. The decoder is assumed to know the value indexed.
  • the scale factor requires bits for coding and is taken into account when bit allocation is performed in block 206 .
  • Each subband value is divided by the scale factor value corresponding to the subband.
  • the scaled subband samples are quantized by quantizers whose step sizes are determined by the SMA and SNR values.
  • the bits resulting from the quantization process are packed to conform to the MPEG audio bit stream definitions in the case of MPEG or any other standard that is used.
  • United States Patent No. 5,185,800 discloses a bit allocation device for transformed digital audio signals with adaptive quantization based on psychoauditive criterion. More information on quantizing and encoding also may be found in Ziemer et al, Signals and Systems: Continuous and Discrete, Macmillian Publishing Co. (2d ed. 1989).
  • Layers I and II split the signal into 32 uniformly spaced subbands using a cosine modulated filter bank as specified in ISO-IEC/JTC1 SC29/WG11, Coding Of Moving Pictures And Associated Audio for Digital Storage Media At Up to About 1.5 Mbits/s - Part 3: Audio, DIS, 11172, April 1992.
  • Layer III also uses 32 subbands in the initial stage but further splitting is performed within the subbands to obtain subband samples of finer frequency divisions.
  • the 384 samples are grouped together in a frame and a new bit-allocation table is computed for each of these frames.
  • the psychoacoustic models use a 512-point discrete Fourier transform (DFT) to compute the spectrum. For the permitted samples rates of 32, 44.1 and 48 kHz, this translates into the requirement of performing bit-allocation computation for each 12, 8.7 and 8 milliseconds. For Layer II, 1152 (3x384) samples are grouped together in a frame and a 1024-point DFT is used for spectral analysis.
  • DFT discrete Fourier transform
  • the computational requirement for computing the PM2 while Layer II is employed can be derived as 26,314 multiplies, 37,341 adds, 1024 compares, 1135 logarithms, 1201 table index operations, 859 divides, 768 square roots and 512 inverse tangents per 6 ms or approximately 170 times a second for a two-channel (stereo) audio. See ISO-IEC/JTC1 SC29/WG11, Comments On Audio CD And Analysis Of Audio Complexity, May 1991 for more information.
  • a preferred embodiment of the present invention provides a process for bit allocation that can be computationally 70 times more efficient than the PM2 for Layer II, and about 60 times more efficient than PM1 for Layer I.
  • the present invention is well suited for use with standard digital processor architectures.
  • the present invention in the preferred embodiment, predicts SMR values based on the energy in a subband rather than by spectral analysis as depicted in Figure 1 .
  • the subbands obtained from subband analysis are utilized to predict the SMR value utilized in bit allocation.
  • the subband energy is employed in accordance with a preferred embodiment of the present invention.
  • the prediction of the SMR value is accomplished by utilizing a matrix of prediction coefficients indexed by subbands.
  • the prediction coefficients are found by utilizing actual psychoacoustic models, such as PM1 and PM2. Details of the methodology used in accordance with a preferred embodiment of the present invention are presented in the following sections. Utilizing this approach, dynamic bit allocation schemes for any subband codes may be developed in accordance with a preferred embodiment of the present invention.
  • PM1 and PM2 can be found in ISO-IEC/JTC1 SC29/WG11, Coding Of Moving Pictures And Associated Audio for Digital Storage Media At Up to About 1.5 Mbits/s - Part 3: Audio, DIS, 11172, April 1992.
  • PM1 first the DFT is performed to obtain the power density spectrum of the signal. From the power spectrum, tonal and non-tonal components of the signal are computed since it is well known that these components have different masking characteristics. These masking characteristics can cross the boundaries (or cut-off frequencies) of the subbands. The global masking thresholds at various frequency points are then computed. Minima of these values within each subband represent the SMR.
  • PM2 requires more complex operations using both magnitude and phase of the DFT and is detailed in ISO-IEC/JTC1 SC29/WG11, Coding Of Moving Pictures And Associated Audio for Digital Storage Media At Up to About 1.5 Mbits/s - Part 3: Audio, DIS, 11172, April 1992
  • the subband samples represent the temporal information within their respective bandwidths. Assuming that each subband provides perfect bandpass characteristics, the summation of the square of each subband value within a subband reflects the energy in that frequency band by the application of Parseval's Theorem as described in A.V. Oppenheim and R.W. Schafer, Digital Processing of Signals, Englewood Cliffs, NJ: Prentice Hall, 1979.
  • the analysis filter bank that provides subband decomposition has been designed using a prototype filter that provides more than 96 dB attenuation in the stop band. See K. Brandenberg and G. Stoll, "The ISO/MPEG-Audio codec: A generic standard for coding of high quality digital audio," Proc. of the 92nd Convention of the Audio Engineering Society, Vienna, March 1992 for more information.
  • the problem of finding a linear model translates simply into estimating a matrix of dimension 32 by 33 to map the energy values into an array of SMR values.
  • the initial step is to obtain data for modelling. Once the data is obtained, finding the best model that fits the data is the next step in the process. First, the mechanism for collecting the data will be examined. Next, the appropriate input and output data sets will be selected. Then, the linear hypothesis will be tested to support the arguments for a linear model. Finally, actual estimation of the matrix will be conducted in accordance with a preferred embodiment of the present invention.
  • the data collection procedure requires that a good psychoacoustic model be used to obtain sample SMR values.
  • Software has been used to obtain SMR values via the two psychoacoustic models described in ISO-IEC/JTC1 SC29/WG11, Coding of Moving Pictures And Associated Audio for Digital Storage Media At Up to About 1.5 Mbits/s - Part 3: Audio, DIS, 11172, April 1992.
  • PM1 and PM2 both have been used in experiments.
  • To obtain a set of data for the estimation problem a variety of music and speech signals are needed. A multitude of audio samples from classical and popular music, and some speech signals varying between 20 and 30 seconds of duration was captured in monoaural mode at 44.1 kHz sampling rate with 16 bit resolution per sample using the IBM Audio Capture and Playback Adapter (ACPA).
  • ACPA IBM Audio Capture and Playback Adapter
  • a similar approach may be taken to capture data at 32 and 48 kHz samples techniques as well. If the samples are available digitally, there will be no need for using an audio capturing hardware. A table of time domain energy values in each subband and the corresponding SMR values from an established psychoacoustic model for several frames of audio may be produced using the simulation programs. If the data from all of the different musical samples were to be collected, one would end up with a prohibitively large data set. To circumvent this problem, a sampling technique was employed. A pseudo-random number generator with uniform distribution characteristics was utilized for sampling purposes. Let the random number that lies between 0 and 215 - 1 be denoted by w i .
  • Data corresponding to Layer I and Layer II were obtained using PM1 and also PM2.
  • the absolute values of the subband samples were considered instead of the square of the samples in accordance with a preferred embodiment of the present invention. This was done to minimize the computational or cycle requirements in programmable DSPs.
  • the absolute values of the subband samples are referred to herein as "pseudo-energy" values. While a modelling for Layer 1, in each frame for each subband 12 absolute values of the samples were summed together to obtain the energy value in that subband. While using Layer II, 36 absolute values were summed to obtain the pseudo-energy values.
  • a "frame" contains a number of adjacent audio samples. The aim is to obtain an estimate of ⁇ k,i such that the errors are small for the given data.
  • the number of parameters to be estimated are 32 X 33.
  • the additional 32 parameters come from the requirement to estimate a bias vector that correspond to x 33(.).
  • a SMR values are determined for a random audio sample utilizing a psychoacoustic model, such as PM1 or PM2, as depicted in block 300 . Thereafter energy values for the subbands in the sample are determined, as illustrated in block 302 . Then a prediction coefficient is determined for each subband and data point, as depicted in block 304 . The prediction coefficients are ⁇ k,i as shown in equation (1). Then, a determination of whether more samples are present is made, as illustrated in block 306 . If no more samples are present, the process terminates. Otherwise, the process returns to block 300 to process another audio sample in accordance with a preferred embodiment of the present invention.
  • a psychoacoustic model such as PM1 or PM2
  • MLE maximum likelihood estimate
  • Table 1 Variance Ratio Table for Hypothesis Testing Subband F o F ⁇ ,32,N-33 1 7.66 1.9 7 22.34 1.9 11 55.68 1.9 19 134.06 1.9 24 217.36 1.9 31 159.79 1.9
  • the results in Table 1 are typical for all the data that was gathered; Layer II, Layer I with various combinations of PM1 and PM2. Thus, it is very clear that the null hypothesis should be rejected and estimation should proceed.
  • equation (2) provides the Best Linear Unbiased Estimate of b k under the normality assumptions.
  • outliers the data points at which the errors are considerably larger, and the lack of knowledge about the distribution of the errors
  • estimators may have to be utilized.
  • a typical result of using least squares estimate of b k by plotting the errors for sample frames is illustrated in Figure 3 .
  • Figure 3 it is indeed clear that elimination of certain points can very well contribute to better estimation of b k .
  • a technique known as robust estimation has been deemed as an appropriate alternative to least squares technique in the presence of outliers.
  • the appropriate selection of k from the above estimations is based on two tests, a subjective and an objective one in accordance with a preferred embodiment of the present invention.
  • the music quality is subjectively evaluated against both the original and compressed/decompressed music pieces that were obtained by using either PM1 or PM2.
  • the bit-allocation deviations from a corresponding MPEG implementation using either PM1 or PM2 are employed.
  • the deviations are computed for sampled frames and the average deviation per frame is taken as an indication of the amount of digression from an implementation that uses the recommended psychoacoustic models.
  • PCM Pulse code modulation
  • Table 2 provides a comparison between PM2 and a preferred embodiment of the present invention.
  • Subband No. No. Bits PM2 No. Bits Proposed Deviation Average bits/frame 1 23161 23035 0.242090 2 16361 16970 0.136139 3 13483 13782 0.123077 4 13853 13659 0.211030 5 12425 12550 0.149492 6 13240 13000 0.197097 7 12078 11998 0.169811 8 11769 11503 0.195356 9 11132 10725 0.161393 10 10755 10521 0.109434 11 10558 10451 0.079245 12 9999 10193 0.057765 13 9053 9198 0.109724 14 7168 7159 0.102467 15 5866 6076 0.123948 16 4110 4193 0.137881 17 1740 1559 0.182003 18 54 43 0.028157
  • FIG. 5 a high level flowchart of a process for compressing PCM samples is depicted in accordance with a preferred embodiment of the present invention.
  • Subband analysis is performed on the PCM samples to produce the desired subbands for each samples.
  • Each subband may be produced by filtering the sample utilizing known filtering systems in accordance with a preferred embodiment of the present invention.
  • SMR is predicted for a subband utilizing a model of relationships between energy values within each of the subbands and utilizing SMR values based on a predetermined psychoacoustic model, as illustrated in block 402 .
  • the predicted SMR is employed to determine a bit allocation for the sample, as depicted in block 404 .
  • a desired bit-rate also is considered in bit allocation, as illustrated in block 406 .
  • Scale factor coding is performed for each of the subbands in a PCM sample, as depicted in block 408 .
  • Quantization and bit packing is performed, as illustrated in block 410 , utilizing the bit-allocation and scale factor from blocks 404 and 408 .
  • the need for spectral analysis of PCM samples being compressed is eliminated.
  • FIG. 6 a flowchart of a process for predicting SMR values in block 402 of Figure 5 is illustrated in accordance with a preferred embodiment of the present invention.
  • L is 12 for Layer I and 36 for Layer II under MPEG standards.
  • the process of the present invention may be "tuned" for a specific type of music. For example, if a user sending an audio signal for classic music desires to encode only the classical violin, samples from a classic violin source may be collected in estimating ⁇ i,j 's. The estimated ⁇ i,j 's will be better suited for classical violin. Furthermore, a user may obtain several sets of ⁇ i,j 's corresponding to different types of music and one set may be selected by the user appropriately.
  • squared-energy values for subband samples S i,l may be employed instead of absolute values in equation (6):
  • the constant 'C' can be selected using empirical observations. By attempting to equate X's to normalized sound pressure levels, C can be set to about 82.53 dB.
  • the determination of prediction coefficients, ⁇ i,j , in equation (1) also will replace pseudo-energy values with squared-energy values in accordance with a preferred embodiment of the present invention.
  • data processing system 10 includes a system 12 , a video display terminal 14 , a keyboard 16 , and a mouse 18 .
  • Data processing system 10 may be implemented utilizing any suitable computer, such as an IBM PS/2 or IBM RISC SYSTEM/6000 computer, both products of International Business Machines Corporation, located in Armonk, New York. "PS/2" and "RISC SYSTEM/6000" are trademarks of International Business Machines Corporation.
  • PS/2 and "RISC SYSTEM/6000” are trademarks of International Business Machines Corporation.
  • the depicted embodiment is a personal computer, a preferred embodiment of the present invention may be implemented in other types of data processing systems, such as, for example, intelligent workstations, mini computers, local area networks, or special purpose multimedia devices using standard digital signal processors.
  • System bus 11 provides a connection between various components within data processing system 10 .
  • Central processing unit (CPU) 22 provides the decision making capability in data processing system 10 .
  • CPU 12 may include one or more processors, such as an 80486 processor or a Pentium processor available from Intel Corporation in Santa Clara, California. "Pentium” is a trademark of Intel Corporation. Other processors that may be used include Power PC available from IBM/Motorola or Alpha AXP processors from Digital Equipment.
  • Memory 24 provides a storage for data processing system 10 and may include both read only memory (ROM) and random access memory (RAM).
  • Direct access storage device (DASD) 26 provides additional storage for data processing system 10 .
  • DASD 26 typically provides long term storage for data processing system 10 .
  • DASD 26 may include, for example, a hard disk drive or a floppy disk drive.
  • an audio capture and playback adapter (ACPA) 25 may be employed to obtain audio samples.
  • ACPA audio capture and playback adapter
  • an IBM Audio Capture and Playback Adapter available from International Business Machines Corporation, may be utilized.
  • Popular Sound Blaster and other sound cords may also be utilized. if audio data can be directly read from the CD or DAT, these sources also may be utilized.
  • Communications unit 28 provides the interface between the data processing system 10 and some other data processing system such as another personal computer or a network.
  • the digital audio signal processed by the present invention may originate from stored data in DASD 26 , or may be received at communications unit 28 , or from some other source of data that is connected to the data processing system, such as ACPA 25 .
  • a preferred embodiment of the present invention may be implemented in an IBM RISC SYSTEM/6000 computer, which is a product of International Business Machines Corporation, located in Armonk, New York. "RISC SYSTEM/6000" is a trademark of International Business Machines Corporation.
  • the processes of the present invention may be implemented within the data processing system depicted in Figures 7 and 8 or in hardware.
  • the present invention allows a simpler implementation than the process depicted in Figure 1 .
  • the present invention also may be utilized with psychoacoustic models other than those specified by MPEG.
  • the operation can be performed in one instruction cycle. Furthermore, in a fixed-point DSP, the truncation can be made to the result in the accumulator at the end of all addition, thus preventing round-off errors after each accumulation.
  • process of the present invention is faster because the number of instruction cycles required are much less than a process utilizing PM1 or PM2 in a standard DSP environment in accordance with a preferred embodiment of the present invention.
  • the performance gains provided by the present invention provides a more efficient encoding process for data.
  • the a preferred embodiment of the present invention may be implemented with a single DSP.
  • subband coding employing a psychovisual weighting may be implemented in accordance with a preferred embodiment of the present invention.

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EP94308601A 1993-12-30 1994-11-22 Codage perceptuel en sous-bandes dans laquelle le rapport signal/masquage est calculés à partir des signaux dans les sous-bandes Withdrawn EP0661826A2 (fr)

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